The present embodiments relate to coding digital image data and a corresponding decoding.
Increasing resolution and quality requirements on visual content, like images, videos, or multi-dimensional medical data raise the demand for highly efficient coding methods. In predictive coding techniques, the pixel values of pixels in the image data are predicted. The difference between the predicted pixel values and the original pixel values (e.g., the prediction error) is compressed, thus forming a part of the coded image data.
Different variants of piecewise autoregressive pixel-prediction methods are provided. In those methods, a pixel value of a current pixel to be predicted is calculated based on a weighted sum of reconstructed, previously processed pixels in a neighborhood region adjacent to the current pixel. In order to determine the weights, a system of linear equations based on the weighted sums for known pixel values in a training region adjacent to the current pixel is solved.
For a precise prediction, piecewise autoregressive pixel-prediction methods use a large causal neighborhood region of known reconstructed pixels around the current pixel. Usually, such a large neighborhood region is not available for all image positions (e.g., at image borders). This problem becomes worse if image regions are to be coded independently from each other, as is the case for a block-wise processing in parallel coding implementations.
The above described border problem occurring in piecewise autoregressive pixel-prediction methods is often not addressed in prior art publications, or border regions are skipped when using autoregressive pixel-prediction methods. A direct way to address this problem without an algorithmic change is an image padding at border regions (e.g., with known values of already transmitted border pixel values; constant border extension). A reduction of the training region size at border positions may be provided. However, this leads to an over-fitting and often causes badly conditioned systems of linear equations. Another option for handling border regions is a special border pixel treatment using different prediction schemes with relaxed context requirements like median prediction. Such special treatment requires additional implementation effort, leads to inhomogeneous predictions, and may often considerably jeopardize prediction accuracy.